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Erschienen in:
On Revenue Monotonicity in Combinatorial Auctions Kapitel lesen Erstes Kapitel lesen

2018 | OriginalPaper | Buchkapitel

Information Elicitation for Bayesian Auctions

verfasst von : Jing Chen, Bo Li, Yingkai Li

Erschienen in: Algorithmic Game Theory

Verlag: Springer International Publishing

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Abstract

In this paper we design information elicitation mechanisms for Bayesian auctions. While in Bayesian mechanism design the distributions of the players’ private types are often assumed to be common knowledge, information elicitation considers the situation where the players know the distributions better than the decision maker. To weaken the information assumption in Bayesian auctions, we consider an information structure where the knowledge about the distributions is arbitrarily scattered among the players. In such an unstructured information setting, we design mechanisms for auctions with unit-demand or additive valuation functions that aggregate the players’ knowledge, generating revenue that are constant approximations to the optimal Bayesian mechanisms with a common prior. Our mechanisms are 2-step dominant-strategy truthful and the revenue increases gracefully with the amount of knowledge the players collectively have.

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Vorheriges Kapitel A Near Optimal Mechanism for Energy Aware Scheduling
Nächstes Kapitel Coreness of Cooperative Games with Truncated Submodular Profit Functions
Fußnoten
1
A Bayesian mechanism is BIC if it is a Bayesian Nash equilibrium for all players to report their true values.
 
2
A directed graph is 2-connected if for any node i, the graph with i and all adjacent edges removed is still strongly connected..
 
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Metadaten
Titel
Information Elicitation for Bayesian Auctions
verfasst von
Jing Chen
Bo Li
Yingkai Li
Copyright-Jahr
2018
Buch
Algorithmic Game Theory

Print ISBN: 978-3-319-99659-2

Electronic ISBN: 978-3-319-99660-8

Copyright-Jahr: 2018

DOI
https://doi.org/10.1007/978-3-319-99660-8_5